Triple
T10468773
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jafar |
E246870
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Oded Fehr |
E502077
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Oded Fehr | Statement: [Jafar, portrayedBy, Oded Fehr]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oded Fehr Context triple: [Jafar, portrayedBy, Oded Fehr]
-
A.
Oded Fehr
chosen
Oded Fehr is an Israeli actor best known for his roles in action and horror films such as The Mummy series and the Resident Evil franchise.
-
B.
Ehud Kalai
Ehud Kalai is an Israeli-American game theorist and economist known for his influential contributions to bargaining theory, game theory, and economic theory.
-
C.
Oded Kotler
Oded Kotler is an Israeli actor and theater director known for his prominent roles in film and stage as well as his influential work in Israeli performing arts.
-
D.
Ariel Rubinstein
Ariel Rubinstein is an Israeli economist renowned for his foundational contributions to game theory, particularly his formalization of bargaining through the Rubinstein bargaining model.
-
E.
Uriel Feige
Uriel Feige is an Israeli computer scientist known for his influential work in computational complexity theory, approximation algorithms, and probabilistically checkable proofs.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d381c16c248190a2fe5b471e584e9c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d5092ef810819093a4d1df83aeac09 |
completed | April 7, 2026, 1:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d89ff1cd948190a1ef331fb810bf26 |
completed | April 10, 2026, 7 a.m. |
Created at: April 6, 2026, 12:20 p.m.